Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans

Deshan Kalupahana; Sonit Singh; Praveen Ravindran; Arcot Sowmya

cs.CVOct 2, 2026

Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans

Authors: Deshan Kalupahana, Sonit Singh, Praveen Ravindran, Arcot Sowmya

Organizations: School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · Sydney Adventist Hospital, Wahroonga, Australia

Abstract

Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected predictions due to complex anatomy. This study introduces a three-stage, topology-preserving segmentation pipeline to address this issue. The first stage performs initial deep learning-based segmentation, followed by centreline bridging to reconnect disjoint regions and a reconstruction stage to refine continuity. Evaluations on TotalSegmentator and RAOS datasets using overlap, distance and topology-based metrics demonstrate improved structural consistency while maintaining segmentation accuracy. The proposed method enhances topological integrity, enabling more reliable colon segmentation for clinical and research applications.

Figures & tables

Figure 1: Three-stage pipeline for topology-preserving colon segmentation: (1) initial segmentation with possible disconnections (2) centreline bridging and (3) disconnected region prediction and merging.

UNet Variant	TotalSegmentator	RAOS
nnUNet	0.9308±0.0631	0.8847±0.0706
BowelNet	0.9041±0.0848	0.8457±0.0929
VNet	0.8882±0.0889	0.8348±0.0797
UNet++	0.9118±0.0722	0.8594±0.0735
SwinUNeTR	0.8701±0.0931	0.8513±0.0741
UNetTR	0.8678±0.0892	0.8200±0.0803

Table 1: Dice scores of Stage 1: Initial Colon Segmentation

Figure 2: Samples from the TotalSegmentator and RAOS datasets showing cases with disconnections, and the progressive reconstruction of colon segmentation across pipeline stages compared to the ground truth (GT) annotations.

3D Medical Image Segmentation

3D Medical Image Segmentation identifies organs, lesions, and fine structures in volumetric scans for measurement and clinical planning.

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Medical Image Segmentation 3D Medical Image Segmentation State Space Models Computed Tomography

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Accurate segmentation of aortic dissection (AD) lumens in CT angiography (CTA) is essential for quantitative morphological assessment and clinical decision-making. However, reliable 3D delineation remains challenging due to limited long-range context modeling, which compromises inter-slice coherence, and insufficient structural discrimination under low-contrast conditions. To address these limitations, we propose BiM-GeoAttn-Net, a lightweight framework that integrates linear-time depth-wise state-space modeling with geometry-aware vessel refinement. Our approach is featured by Bidirectional Depth Mamba (BiM) to efficiently capture cross-slice dependencies and Geometry-Aware Vessel Attention (GeoAttn) module that employs orientation-sensitive anisotropic filtering to refine tubular structures and sharpen ambiguous boundaries. Extensive experiments on a multi-source AD CTA dataset demonstrate that BiM-GeoAttn-Net achieves a Dice score of 93.35% and an HD95 of 12.36 mm, outperforming representative CNN-, Transformer-, and SSM-based baselines in overlap metrics while maintaining competitive boundary accuracy. These results suggest that coupling linear-time depth modeling with geometry-aware refinement provides an effective, computationally efficient solution for robust 3D AD segmentation.

Abstract

3D Medical Image Segmentation

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